Application of Genetic Algorithms and Neural Networks to Unsteady Flow Control Optimization
Beliganur, N., R.P. LeBeau, Jr., Dept. of Mechanical Engineering, University of
Kentucky
T. Hauser, Dept. of Mechanical and Aerospace Engineering, Utah State University
Evolutionary algorithms have been successfully used as a design optimization tool in several engineering optimization problems.l Previously, we have successfully tested the Genetic Algorithm - Computational Fluid Dynamics (GA-CFD) and the GA-Neural Network-CFD (GA-NN-CFD) setup to optimize the suction and blowing parameters (Location, amplitude and angle) on a NACA 0012 airfoil, which is a 2D, steady, flow control problem. In this paper, we propose to apply the GA-NN-CFD setup to more realistic flow control optimization problems. Sample problems under consideration include synthetic jets, morphing airfoils, and plasma actuators.